Progressive Agriculture Ranking (PAR)

Progressive Agriculture Ranking (PAR) COMING in EARLY 2017

The Progressive Agriculture Ranking (PAR) ranks all qualifying counties in the United States by how oriented agriculture and food production is to local resources, markets, and residents' interests and quality of life.

We are currently in the process of comparing the PAR data for 2007 and 2012 so we can see how progressiveness in counties changes over time. We hope to map the data and make it available in early 2017.

The Theory Behind PAR

Our assumption in developing the PAR is that, generally speaking, progressive communities want to see “gradual betterment” in local farming by making use of new ideas and opportunities. The theory behind this ranking is simple: the more a given county’s farmers exhibit the characteristics of these progressive farming variables, the more gradual betterment is taking place. Of course, there are lots of ways to operationalize measures of progress. We chose the following indicators (1-6) that are found in the Census of Agriculture.

Methodology

Data for each progressive farming indicator comes from the USDA’s Census of Agriculture, which provides the only national data set of systematically collected information related to the social, economic, and environmental characteristics of county agriculture. Responding to decades of growth in alternative and sustainable agriculture, the USDA collected several new valuable pieces of information from farmers beginning with the 2007 census: energy production, conservation practices, value-adding, and participation in CSAs. Combined with female tenure and organic production, this constellation of variables constitutes the PAR.

To be included in the PAR with statistically valid data, a county had to have at least 100 farms. This reduced the number of counties included in the index by 6.7%, from 3,141 to 2,931.

The method of ranking consisted of:

1Calculating the percent of a county’s farms engaged in each progressive activity or exhibiting each progressive characteristic.

2Ranking the counties from highest percent to lowest. All counties that did not have any farmers reporting a particular progressive farming activity or characteristic received the same dead-last rank. For example, if 1,357 counties could be ranked on a given variable, all remaining counties received the rank of 1,358. If there was a tie, the number was repeated and the following number skipped (e.g., a tie at ranking 12 shows two #12s, and no #13).

3The overall rank was created by averaging a county’s ranks across all six indicators. The county with the highest average rank (and thus lowest number) was given the final rank of #1; the second highest average rank received the final rank of #2; and so on.

Urban Influence Category

Both the elegance and weakness of this ranking is in its simplicity. Its weakness is that in keeping the method simple we have not completely controlled for the uneven regional differences in opportunities. For example, it is possible that not all counties have an equal chance of producing farm-based energy, or establishing CSAs (because highly rural counties have small populations, so their focus is more likely to be on commodities rather than direct marketing).

To account for some of the demographic difference between counties, we’ve used “urban influence codes” (UICs) to group counties with similar marketing opportunities. The codes are as follows:

1 = Large — in a metro area with at least 1 million residents or more

2 = Small — in a metro area with fewer than 1 million residents

3 = Micropolitan area adjacent to a large metro area

4 = Noncore adjacent to a large metro area

5 = Micropolitan area adjacent to a small metro area

6 = Noncore adjacent to a small metro area with town of at least 2,500 residents

7 = Noncore adjacent to a small metro area and does not contain a town of at least 2,500 residents

8 = Micropolitan area not adjacent to a metro area

9 = Noncore adjacent to micro area and contains a town of at least 2,500 residents

10 = Noncore adjacent to micro area and does not contain a town of at least 2,500 residents

11 = Noncore not adjacent to a metro/micro area and contains a town of 2,500 or more residents

12 = Noncore not adjacent to a metro/micro area and does not contain a town of at least 2,500 residents

You can rank the counties by their UICs to see how your county compares to counties with similar urban influences. The codes were developed by the USDA, Economic Research Service (www.ers.usda.gov/briefing/rurality). It should be noted that these codes were assigned to counties in 2003 and it is possible that some counties would be coded differently today.

Location Quotients

In addition to comparing your county to other counties, you may also want to compare your county to the United State as a whole—which is to say, the average of all counties. To aid in comparing a county to the country as a whole, we calculated “location quotients” for each variable in each county. Location Quotients (LQs) for each individual county (LQi) were calculated using the following equation:

LQi = ( Ci / ΣCi ) / ( USi / ΣUSi )

where Ci is equal to the percent of farms engaging in a particular activity (or having a particular characteristic) in County i, and USi is the percent of farms of the United States as a whole engaging in that same activity (or having the same characteristic). The LQ of the U.S. as a whole is 1.0; a county LQ of 1.2 or greater, or .8 or lower is said to have a statistically significant difference from the U.S. as a whole.

Final Comments of the Lyson Index

Despite the complications of making comparisons between varying areas of the U.S., the PAR can provide a lot of value. It is useful for counties that are engaging in progressive agriculture development projects to help in benchmarking and measuring their success in encouraging progressive local agriculture and food systems over time. We intend to update this with 2012 Census of Agriculture data, which will let counties truly gauge their progress over the last decade. We invite your This email address is being protected from spambots. You need JavaScript enabled to view it. on improving the PAR.

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